| Autor: |
Mai Ezz-Eldin, Ashraf A. M. Khalaf, Hesham F. A. Hamed, Aziza I. Hussein |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 9, Pp 19999-20011 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3054345 |
| Popis: |
Robust automatic speech emotional-speech recognition architectures based on hybrid convolutional neural networks (CNN) and feedforward deep neural networks are proposed and named in this paper as: BFN, CNA, and HBN. BFN is a combination between bag-of-Audio-word (BoAW) and feedforward deep neural network, CNA based on CNN, finally, HBN is hybrid architecture between BFN and CNA. Overall accuracy is achieved by leveraging Mel-frequency cepstral coefficient features and bag-of-acoustic-words to feed the network, resulting in promising classification performance. In addition, the concatenated output from the proposed hybrid networks is fed into a softmax layer to produce a probability distribution over categorical classifications for speech recognition. The three proposed models are trained on eight emotional classes from the Ryerson Audio-Visual Database of Emotional Speech and Song audio (RAVDESS) dataset. Our proposed models achieved overall precision between 81.5% and 85.5% and overall accuracy between 80.6% and 84.5%, hence outperforming state-of-the-art models using the same dataset. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
